The text discusses a new approach to using the Model Context Protocol (MCP) for AI agents by converting MCP tools into a TypeScript API, allowing large language models (LLMs) to write code that calls these APIs. This method significantly enhances the capability of agents to manage a wider range of tools, particularly complex ones, by leveraging the extensive amount of real-world TypeScript data in LLMs' training sets. Traditional MCP usage involves directly exposing tools to LLMs, which can be inefficient and limit the number of tools an agent can handle. The new approach avoids this by enabling the LLM to generate code that interacts with the MCP server, bypassing the need for the LLM to process each tool call individually and thereby conserving resources. This method utilizes the Cloudflare Workers platform, employing lightweight isolates rather than containers for executing code in secure sandboxes, which offers faster and more cost-effective deployment. The Worker Loader API facilitates this process by allowing on-demand code execution in isolated environments, enhancing security by using bindings that manage connectivity and authorization without exposing API keys. This development aims to streamline AI agent operations, making them more efficient and capable of handling complex tasks through standardized and secure API interactions.